13 articles
Artificial immune algorithm
An artificial immune algorithm, or immune algorithm, is a bio-inspired optimization method mimicking clonal selection and hypermutation, applied to multimodal optimization, machine learning, and pattern recognition.
Artificial immune system
An artificial immune system (AIS) is a class of bio-inspired algorithms that turns immune models such as clonal and negative selection into tools for optimization, learning, and anomaly detection.
Biogeography-based optimization
Biogeography-based optimization (BBO) is a population-based evolutionary algorithm for global optimization, introduced by Dan Simon in 2008, which mimics species migration, speciation, and extinction between habitats.
Clonal selection algorithm
The clonal selection algorithm is an optimization method inspired by the immune system, cloning and mutating the best candidate solutions; the best-known version is CLONALG.
Covariance matrix adaptation evolution strategy (CMA-ES)
Covariance matrix adaptation evolution strategy (CMA-ES) is a stochastic evolutionary algorithm for black-box minimization of continuous, non-convex functions, introduced by Nikolaus Hansen and Andreas Ostermeier in 2001.
Evolution strategy
An evolution strategy is a black-box optimization algorithm that improves candidate solutions through mutation, recombination, and selection, developed by Ingo Rechenberg and Hans-Paul Schwefel in the 1970s.
Evolutionary computation
Evolutionary computation is a group of search and optimization techniques that mimics biological evolution, maintaining populations of candidate solutions that improve through selection, mutation, and recombination.
Evolutionary programming
Evolutionary programming is a family of stochastic optimization algorithms that evolves candidate solutions through mutation and selection alone, without crossover, conceived by Lawrence J. Fogel in 1960.
Interactive genetic algorithm
An interactive genetic algorithm is a genetic algorithm in which a human user evaluates candidate solutions instead of a fitness function, applied to art, music, and design tasks.
Memetic algorithm
A memetic algorithm is a population-based metaheuristic combining an evolutionary framework with local-search operators that refine individual solutions, introduced by Pablo Moscato in 1989 for hard optimization problems.
Premature convergence
Premature convergence is a failure mode of evolutionary algorithms in which the population loses diversity and settles too early on a suboptimal solution, usually a local optimum.
Quantum genetic algorithm
A quantum genetic algorithm is an evolutionary optimization method that encodes candidate solutions as quantum-inspired probabilistic bits, updated by rotation gates, and runs on ordinary classical computers.
Scatter search
Scatter search (SS) is a population-based evolutionary metaheuristic for optimization, introduced by Fred Glover in 1977, which combines solutions systematically rather than randomly as genetic algorithms do.